• Students are able to find their way around selected special areas of management within the scope of business management.
• Students are able to explain basic theories, categories, and models in selected special areas of business management.
• Students are able to interrelate technical and management knowledge.

• Students are able to apply basic methods in selected areas of business management.
• Students are able to explain and give reasons for decision proposals on practical issues in areas of business management.

• Students are able to communicate in small interdisciplinary groups and to jointly develop solutions for complex problems

The Nontechnical Academic Programms (NTA)

imparts skills that, in view of the TUHH’s training profile, professional engineering studies require but are not able to cover fully. Self-reliance, self-management, collaboration and professional and personnel management competences. The department implements these training objectives in its teaching architecture, in its teaching and learning arrangements, in teaching areas and by means of teaching offerings in which students can qualify by opting for specific competences and a competence level at the Bachelor’s or Master’s level. The teaching offerings are pooled in two different catalogues for nontechnical complementary courses.

The Learning Architecture

consists of a cross-disciplinarily study offering. The centrally designed teaching offering ensures that courses in the nontechnical academic programms follow the specific profiling of TUHH degree courses.

The learning architecture demands and trains independent educational planning as regards the individual development of competences. It also provides orientation knowledge in the form of “profiles”.

The subjects that can be studied in parallel throughout the student’s entire study program - if need be, it can be studied in one to two semesters. In view of the adaptation problems that individuals commonly face in their first semesters after making the transition from school to university and in order to encourage individually planned semesters abroad, there is no obligation to study these subjects in one or two specific semesters during the course of studies.

Teaching and Learning Arrangements

provide for students, separated into B.Sc. and M.Sc., to learn with and from each other across semesters. The challenge of dealing with interdisciplinarity and a variety of stages of learning in courses are part of the learning architecture and are deliberately encouraged in specific courses.

Fields of Teaching

are based on research findings from the academic disciplines cultural studies, social studies, arts, historical studies, communication studies, migration studies and sustainability research, and from engineering didactics. In addition, from the winter semester 2014/15 students on all Bachelor’s courses will have the opportunity to learn about business management and start-ups in a goal-oriented way.

The fields of teaching are augmented by soft skills offers and a foreign language offer. Here, the focus is on encouraging goal-oriented communication skills, e.g. the skills required by outgoing engineers in international and intercultural situations.

The Competence Level

of the courses offered in this area is different as regards the basic training objective in the Bachelor’s and Master’s fields. These differences are reflected in the practical examples used, in content topics that refer to different professional application contexts, and in the higher scientific and theoretical level of abstraction in the B.Sc.

This is also reflected in the different quality of soft skills, which relate to the different team positions and different group leadership functions of Bachelor’s and Master’s graduates in their future working life.

Specialized Competence (Knowledge)

Students can

• explain specialized areas in context of the relevant non-technical disciplines,
• outline basic theories, categories, terminology, models, concepts or artistic techniques in the disciplines represented in the learning area,
• different specialist disciplines relate to their own discipline and differentiate it as well as make connections, 
• sketch the basic outlines of how scientific disciplines, paradigms, models, instruments, methods and forms of representation in the specialized sciences are subject to individual and socio-cultural interpretation and historicity,
• Can communicate in a foreign language in a manner appropriate to the subject.

Professional Competence (Skills)

In selected sub-areas students can

• apply basic and specific methods of the said scientific disciplines,
• aquestion a specific technical phenomena, models, theories from the viewpoint of another, aforementioned specialist discipline,
• to handle simple and advanced questions in aforementioned scientific disciplines in a sucsessful manner,
• justify their decisions on forms of organization and application in practical questions in contexts that go beyond the technical relationship to the subject.

Personal Competences (Social Skills)

Students will be able

• to learn to collaborate in different manner,
• to present and analyze problems in the abovementioned fields in a partner or group situation in a manner appropriate to the addressees,
• to express themselves competently, in a culturally appropriate and gender-sensitive manner in the language of the country (as far as this study-focus would be chosen), 
• to explain nontechnical items to auditorium with technical background knowledge.

The students possess an in-depth knowledge regarding the derivation of the finite element method and are able to give an overview of the theoretical and methodical basis of the method.

The students are capable to handle engineering problems by formulating suitable finite elements, assembling the corresponding system matrices, and solving the resulting system of equations.

Students can work in small groups on specific problems to arrive at joint solutions.

• Students can explain how linear dynamic systems are represented as state space models; they can interpret the system response to initial states or external excitation as trajectories in state space
• They can explain the system properties controllability and observability, and their relationship to state feedback and state estimation, respectively
• They can explain the significance of a minimal realisation
• They can explain observer-based state feedback and how it can be used to achieve tracking and disturbance rejection
• They can extend all of the above to multi-input multi-output systems
• They can explain the z-transform and its relationship with the Laplace Transform
• They can explain state space models and transfer function models of discrete-time systems
• They can explain the experimental identification of ARX models of dynamic systems, and how the identification problem can be solved by solving a normal equation
• They can explain how a state space model can be constructed from a discrete-time impulse response
  
  

• Students can transform transfer function models into state space models and vice versa
• They can assess controllability and observability and construct minimal realisations
• They can design LQG controllers for multivariable plants
•  They can carry out a controller design both in continuous-time and discrete-time domain, and decide which is  appropriate for a given sampling rate
• They can identify transfer function models and state space models of dynamic systems from experimental data
• They can carry out all these tasks using standard software tools (Matlab Control Toolbox, System Identification Toolbox, Simulink)
  
  

Students can work in small groups on specific problems to arrive at joint solutions. 

Students demonstrate basic knowledge and understanding of modeling, simulation and analysis of complex rigid and flexible multibody systems and methods for optimizing dynamic systems after successful completion of the module.

Students are able

+ to think holistically

+ to independently, securly and critically analyze and optimize basic problems of the dynamics of rigid and flexible multibody systems

+ to describe dynamics problems mathematically

+ to optimize dynamics problems

Students are able to

+ solve problems in heterogeneous groups and to document the corresponding results.

• Students can explain the difference between validation of a control lop in simulation and experimental validation

• Students are capable of applying basic system identification tools (Matlab System Identification Toolbox) to identify a dynamic model that can be used for controller synthesis
• They are capable of using standard software tools (Matlab Control Toolbox) for the design and implementation of LQG controllers
• They are capable of using standard software tools (Matlab Robust Control Toolbox) for the mixed-sensitivity design and the implementation of H-infinity optimal controllers
• They are capable of representing model uncertainty, and of designing and implementing a robust controller
• They are capable of using standard software tools (Matlab Robust Control Toolbox) for the design and the implementation of LPV gain-scheduled controllers

• Students can work in teams to conduct experiments and document the results

In this module, students learn the fundamental concepts of nonlinear continuum mechanics. This theory enables students to describe arbitrary deformations of continuous bodies (solid, liquid or gaseous) under arbitrary loads. The module is a continuation of the basic module Engineering Mechanics II (elastostatics), the limiting assumptions (isotropic, linear-elastic material behavior, small deformations, simple geometries) of which are successively eliminated.

First, the students learn the necessary fundamentals of tensor calculus. Based on this, the description of the deformations / strains of arbitrarily deformable bodies is dealt with. The students learn the mathematical formalism for characterizing the stress state of a body and for formulating the balance equations for mass, momentum, energy and entropy in various forms. Furthermore, the students know which constitutive assumptions have to be made for modeling the material behavior of a mechanical body.

The students can set up balance laws and apply basics of deformation theory to specific aspects, both in applied contexts as in research contexts.

The students are able to develop solutions also for complex problems of solid mechanics, to present them to specialists in written form and to develop ideas further.

• Students are able to denote terms and concepts of Vibration Theory and develop them further.
• Students know methods of modeling and simulation for free, driven, self-excited and parameter driven vibrations.
• Students know about concepts of linear and nonlinear vibration problems.
• Students know basic tasks of vibration problems of discrete and continuous systems.

• Students are able to denote methods of Vibration Theory and develop them further.
• Students are able to apply and expand methods of modeling and simulation for free, forced, self-excited and parameter driven vibrations.
• Students are able to solve linear and nonlinear vibration problems.

• Students can analyze vibration problems, work on them, and reach working results also in teams or groups.
• Students are able to document the results of vibration studies also in groups.

Students are able to

• list numerical methods for the solution of ordinary differential equations and explain their core ideas,
• formulate convergence statements for the treated numerical methods (including the assumptions about the underlying problem),
• explain aspects regarding the practical realisation of a method.
• select the appropriate numerical method for concrete problems, implement the numerical algorithms efficiently and interpret the numerical results
  

Students are able to

• implement, apply and compare numerical methods for the solution of ordinary differential equations,
• justify the convergence behaviour of numerical methods with respect to the posed problem and selected algorithm,

• develop a suitable solution approach for a given problem, if necessary by combining of several algorithms, and to realise this approach and critically evaluate the results.

  
  

Students are able to

• work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.

Students can represent the most important methods of dynamics after successful completion of the module Technical dynamics and have a good understanding of the main concepts in the technical dynamics.

Students are able

+ to think holistically

+ to independently, securly and critically analyze and optimize basic problems of the dynamics of rigid and flexible multibody systems

+ to describe dynamics problems mathematically

+ to investigate dynamics problems both experimentally and numerically

Students are able to

+ solve problems in heterogeneous groups and to document the corresponding results.

• Students are able to reflect existing terms and concepts in Nonlinear Dynamics and to develop and research new terms and concepts.
• Students are able to denote and expand methods of modeling and analysis for nonlinear dynamical systems.

• Students are able to apply existing methods and procesures of Nonlinear Dynamics.
• Students are able to develop novel methods and procedures for nonlinear dynamical systems.

• Students can analyze problems of nonlinear dynamics also in groups.
• Students can achieve solution procedures for problems of nonlinear dynamical systems also in groups.

• Students can explain the general framework of the prediction error method and its application to a variety of linear and nonlinear model structures
• They can explain how multilayer perceptron networks are used to model nonlinear dynamics
• They can explain how an approximate predictive control scheme can be based on neural network models
• They can explain the idea of subspace identification and its relation to Kalman realisation theory

• Students are capable of applying the predicition error method to the experimental identification of linear and nonlinear models for dynamic systems
• They are capable of implementing a nonlinear predictive control scheme based on a neural network model
• They are capable of applying subspace algorithms to the experimental identification of linear models for dynamic systems
• They can do the above using standard software tools (including the Matlab System Identification Toolbox)

Students can work in mixed groups on specific problems to arrive at joint solutions. 

Students will acquire a deeper knowledge of computational fluid dynamics (CFD) and can translate general principles of thermo-/fluid engineering into discrete algorithms on the basis of finite volume methods. They are familiar with the similarities and differences between different discretisation and approximation concepts for investigating coupled systems of non-linear, convective partial differential equations (PDE) on structured and unstructured grids. Students have the required background knowledge to develop, code and apply  modelling concepts to numerically describe turbulent and multiphase flow. They establish a thorough understanding of details of the theoretical background of complex CFD algorithms and the parameters used to control and adjust the execution of CFD procedures.

The students are able choose and apply appropriate finite volume (FV) approximation concepts and flow physics models that integrate the governing thermofluid dynamic PDEs in space and time. They can apply/optimise FV concepts to/for fluid dynamic applications. They acquire the ability to code computational algorithms dedicated to unstructured grid arrangements, apply these codes for parameter investigations and supplement interfaces to extract simulation data for an engineering analysis. They are able to judge different solution strategies.

The students are able to discuss problems, present the results of their own analysis, and jointly develop, implement and report on solution strategies that address given technical reference problems in a team.

• Students can explain the significance of the matrix Riccati equation for the solution of LQ problems.
• They can explain the duality between optimal state feedback and optimal state estimation.
• They can explain how the H2 and H-infinity norms are used to represent stability and performance constraints.
• They can explain how an LQG design problem can be formulated as special case of an H2 design problem.
• They  can explain how model uncertainty can be represented in a way that lends itself to robust controller design
• They can explain how - based on the small gain theorem - a robust controller can guarantee stability and performance for an uncertain plant.
• They understand how analysis and synthesis conditions on feedback loops can be represented as linear matrix inequalities.

• Students are capable of designing and tuning LQG controllers for multivariable plant models.
• They are capable of representing a H2 or H-infinity design problem in the form of a generalized plant, and of using standard software tools for solving it.
• They are capable of translating time and frequency domain specifications for control loops into constraints on closed-loop sensitivity functions, and of carrying out a mixed-sensitivity design.
• They are capable of constructing an LFT uncertainty model for an uncertain system, and of designing a mixed-objective robust controller.
• They are capable of formulating analysis and synthesis conditions as linear matrix inequalities (LMI), and of using standard LMI-solvers for solving them.
• They can carry out all of the above using standard software tools (Matlab robust control toolbox).

• Design optimization
  • Gradient based methods
  • Genetic algorithms
  • Optimization with constraints
  • Topology optimization
• Reliability analysis
  • Stochastic basics
  • Monte Carlo methods
  • Semi-analytic approaches
• robust design optimization
  • Robustness measures
  • Coupling of design optimization and reliability analysis

• Application of optimization algorithms and probabilistic methods in the design of structures
• Programming with Matlab
• Implementation of algorithms
• Debugging

• Team work
•  Oral explanation of the the work

Students are able to
+ give an overview of the different (h, p, hp) finite element procedures.
+ explain high-order finite element procedures.
+ specify problems of finite element procedures, to identify them in a given situation and to explain their mathematical and mechanical background.

Students are able to
+ apply high-order finite elements to problems of structural mechanics.
+ select for a given problem of structural mechanics a suitable finite element procedure.
+ critically judge results of high-order finite elements.
+ transfer their knowledge of high-order finite elements to new problems.

Students are able to

• name advanced numerical methods for interpolation, approximation, integration, eigenvalue problems, eigenvalue problems, nonlinear root finding problems and explain their core ideas,
• repeat convergence statements for the numerical methods, sketch convergence proofs,
• explain practical aspects of numerical methods concerning runtime and storage needs
• explain aspects regarding the practical implementation of numerical methods with respect to computational and storage complexity.
  
  

Students are able to

• implement, apply and compare advanced numerical methods in Python,
• justify the convergence behaviour of numerical methods with respect to the problem and solution algorithm and to transfer it to related problems,
• for a given problem, develop a suitable solution approach, if necessary through composition of several algorithms, to execute this approach and to critically evaluate the results
  

Students are able to

• work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge), explain theoretical foundations and support each other with practical aspects regarding the implementation of algorithms.

• Students can name the basic concepts in Stochastics. They are able to explain them using appropriate examples.
• Students can discuss logical connections between these concepts.  They are capable of illustrating these connections with the help of examples.
• They know proof strategies and can reproduce them.

• Students can model problems from stochastics with the help of the concepts studied in this course. Moreover, they are capable of solving them by applying established methods.
  
• Students are able to discover and verify further logical connections between the concepts studied in the course.
• For a given problem, the students can develop and execute a suitable approach, and are able to critically evaluate the results.

• Students are able to work together (e.g. on their regular home work) in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to present their results appropriately (e.g. during exercise class).

• In doing so, they can communicate new concepts according to the needs of their cooperating partners. Moreover, they can design examples to check and deepen the understanding of their peers.

Teaching subject ist the design of simple optical systems for illumination and imaging optics

• Basic values for optical systems and lighting technology
• Spectrum, black-bodies, color-perception
• Light-Sources und their characterization
• Photometrics
• Ray-Optics
• Matrix-Optics
• Stops, Pupils and Windows
• Light-field Technology
• Introduction to Wave-Optics
• Introduction to Holography

Understandings of optics as part of light and electromagnetic spectrum. Design rules, approach to designing optics

The students are able to demonstrate their detailed knowledge in the field of theoretical mechanical engineering. They can exemplify the state of technology and application and discuss critically in the context of actual problems and general conditions of science and society.

The students can develop solving strategies and approaches for fundamental and practical problems in theoretical mechanical engineering. They may apply theory based procedures and integrate safety-related, ecological, ethical, and economic view points of science and society.

The students are able to independently select methods for the project work and to justify this choice. They can explain how these methods relate to the field of work and how the context of application has to be adjusted. General findings and further developments may essentially be outlined.

The students are able to condense the relevance and the structure of the project work, the work steps and the sub-problems for the presentation and discussion in front of a bigger group. They can lead the discussion and give a feedback on the project to their colleagues.

After successful completion of the module students demonstrate deeper knowledge and understanding in selected application areas of multibody dynamics and robotics

Students are able

+ to think holistically

+ to independently, securly and critically analyze and optimize basic problems of the dynamics of rigid and flexible multibody systems

+ to describe dynamics problems mathematically

+ to implement dynamical problems on hardware

Students are able to

+ solve problems in heterogeneous groups and to document the corresponding results and present them

Team Work, joined presentation of results

The students can describe the materials of the human body and the materials being used in medical engineering, and their fields of use.

The students can explain the advantages and disadvantages of different kinds of biomaterials.

The students are able to discuss issues related to materials being present or being used for replacements with student mates and the teachers.

Students can explain the basic principles, relationships, and methods of bioelectromagnetics, i.e. the quantification and application of electromagnetic fields in biological tissue. They can define and exemplify the most important physical phenomena and order them corresponding to wavelength and frequency of the fields. They can give an overview over measurement and numerical techniques for characterization of electromagnetic fields in practical applications . They can give examples for therapeutic and diagnostic utilization of electromagnetic fields in medical technology.

Students know how to apply various methods to characterize the behavior of electromagnetic fields in biological tissue.  In order to do this they can relate to and make use of the elementary solutions of Maxwell’s Equations. They are able to assess the most important effects that these models predict for biological tissue, they can order the effects corresponding to wavelength and frequency, respectively, and they can analyze them in a quantitative way. They are able to develop validation strategies for their predictions. They are able to evaluate the effects of electromagnetic fields for therapeutic and diagnostic applications and make an appropriate choice.

Students are able to work together on subject related tasks in small groups. They are able to present their results effectively in English (e.g. during small group exercises).

• Students can explain humanoid robots.
• Students can explain the basic concepts, relationships and methods of forward- and inverse kinematics
• Students learn to apply basic control concepts for different tasks in humanoid robotics.

• Students can implement models for humanoid robotic systems in Matlab and C++, and use these models for robot motion or other tasks.
• They are capable of using models in Matlab for simulation and testing these models if necessary with C++ code on the real robot system.
• They are capable of selecting methods for solving abstract problems, for which no standard methods are available, and apply it successfully.

• Students can develop joint solutions in mixed teams and present these.
• They can provide appropriate feedback to others, and  constructively handle feedback on their own results

Students can:

• Describe the system configuration and components of the main clinical imaging systems;
• Explain how the system components and the overall system of the imaging systems function;
• Explain and apply the physical processes that make imaging possible and use with the fundamental physical equations; 
• Name and describe the physical effects required to generate image contrasts; 
• Explain how spatial and temporal resolution can be influenced and how to characterize the images generated;
• Explain which image reconstruction methods are used to generate images;

Describe and explain the main clinical uses of the different systems.

Students are able to: 

• Explain the physical processes of images and assign to the systems the basic mathematical or physical equations required;
  • Calculate the parameters of imaging systems using the mathematical or physical equations;
  • Determine the influence of different system components on the spatial and temporal resolution of imaging systems;
  • Explain the importance of different imaging systems for a number of clinical applications;

Select a suitable imaging system for an application.

The students can name the different kinds of artificial limbs.

The students can explain the advantages and disadvantages of different kinds of endoprotheses.

The students are able to discuss issues related to endoprothese with student mates and the teachers.

The students can explain kinematics and tracking systems in clinical contexts and illustrate systems and their components in detail. Systems can be evaluated with respect to collision detection and  safety and regulations. Students can assess typical systems regarding design and  limitations.

The students are able to design and evaluate navigation systems and robotic systems for medical applications.

The students are able to grasp practical tasks in groups, develop solution strategies independently, define work processes and work on them collaboratively.
The students are able to collaboratively organize their work processes and software solutions using virtual communication and software management tools.
The students can critically reflect on the results of other groups, make constructive suggestions for improvement, and also incorporate them into their own work.

After successful completion of the module, students are able to describe reconstruction methods for different tomographic imaging modalities such as computed tomography and magnetic resonance imaging. They know the necessary basics from the fields of signal processing and inverse problems and are familiar with both analytical and iterative image reconstruction methods. The students have a deepened knowledge of the imaging operators of computed tomography and magnetic resonance imaging.

The students are able to implement reconstruction methods and test them using tomographic measurement data. They can visualize the reconstructed images and evaluate the quality of their data and results. In addition, students can estimate the temporal complexity of imaging algorithms.

Students can work on complex problems both independently and in teams. They can exchange ideas with each other and use their individual strengths to solve the problem.

The students know about the most important technologies and materials of MEMS as well as their applications in sensors and actuators.

Students are able to analyze and describe the functional behaviour of MEMS components and to evaluate the potential of microsystems.

Students are able to solve specific problems alone or in a group and to present the results accordingly.

The students are able to analyze and solve clinical treatment planning and decision support problems using methods for search, optimization, and planning. They are able to explain methods for classification and their respective advantages and disadvantages in clinical contexts. The students can compare  different methods for representing medical knowledge. They can evaluate methods in the context of clinical data  and explain challenges due to the clinical nature of the data and its acquisition and due to privacy and safety requirements.

The students can give reasons for selecting and adapting methods for classification, regression, and prediction. They can assess the methods based on actual patient data and evaluate the implemented methods.

The students are able to grasp practical tasks in groups, develop solution strategies independently, define work processes and work on them collaboratively.
The students can critically reflect on the results of other groups, make constructive suggestions for improvement and also incorporate them into their own work.

Students are able to give an overview of conventional and modern electric power systems.  They can explain in detail and critically evaluate technologies of electric power generation, transmission, storage, and distribution as well as integration of equipment into electric power systems.

With completion of this module the students are able to apply the acquired skills in applications of the design, integration, development of electric power systems and to assess the results.

The students can participate in specialized and interdisciplinary discussions, advance ideas and represent their own work results in front of others.

Students know the different energy conversion stages and the difference between efficiency and annual efficiency. They have increased knowledge in heat and mass transfer, especially in regard to buildings and mobile applications. They are familiar with German energy saving code and other technical relevant rules. They know to differ different heating systems in the domestic and industrial area and how to control such heating systems. They are able to model a furnace and to calculate the transient temperatures in a furnace. They have the basic knowledge of emission formations in the flames of small burners  and how to conduct the flue gases into the atmosphere. They are able to model thermodynamic systems with object oriented languages.

Students are able to calculate the heating demand for different heating systems and to choose the suitable components. They are able to calculate a pipeline network and have the ability to perform simple planning tasks, regarding solar energy. They can write Modelica programs and can transfer research knowledge into practice. They are able to perform scientific work in the field of thermal engineering.

In lectures and exercises, the students can use many examples and experiments to discuss in small groups in a goal-oriented manner, develop a solution and present it. Within the exercises, the students can independently develop further questions and work out targeted solutions.

With the completion of this module, students will be able to deal with technical foundations and current issues and problems in the field of solar energy and explain and evaulate these critically in consideration of the prior curriculum and current subject specific issues. In particular they can professionally describe the processes within a solar cell and explain the specific features of application of solar modules. Furthermore, they can provide an overview of the collector technology in solar thermal systems.

Students can apply the acquired theoretical foundations of exemplary energy systems using solar radiation. In this context, for example they can assess and evaluate potential and constraints of solar energy systems with respect to different geographical assumptions. They are able to dimension solar energy systems in consideration of technical aspects and given assumptions. Using module-comprehensive knowledge students can evalute the economic and ecologic conditions of these systems. They can select calculation methods within the radiation theory for these topics. 

Students are able to discuss issues in the thematic fields in the renewable energy sector addressed within the module.

The students can

• distinguish the physical phenomena of conversion of energy,
• understand the different mathematic modelling of turbomachinery,
• calculate and evaluate turbomachinery.
  

The students are able to

- understand the physics of Turbomachinery,

- solve excersises self-consistent.

The students are able to

• discuss in small groups and develop an approach.

Students know the different kinds of air conditioning systems for buildings and mobile applications and how these systems are controlled. They are familiar with the change of state of humid air and are able to draw the state changes in a h1+x,x-diagram. They are able to calculate the minimum airflow needed for hygienic conditions in rooms and can choose suitable filters. They know the basic flow pattern in rooms and are able to calculate the air velocity in rooms with the help of simple methods. They know the principles  to calculate an air duct network. They know the different possibilities to produce cold and are able to draw these processes into suitable thermodynamic diagrams. They know the criteria for the assessment of refrigerants.

Students are able to configure air condition systems for buildings and mobile applications.  They are able to calculate an air duct network and have the ability to perform simple planning tasks, regarding natural heat sources and heat sinks. They can transfer research knowledge into practice. They are able to perform scientific work in the field of air conditioning.

In lectures and exercises, the students can use many examples and experiments to discuss in small groups in a goal-oriented manner, develop a solution and present it. Within the exercises, the students can independently develop further questions and work out targeted solutions.

After successful completion of the module the students are able to

• explain the the basic principles of statistical thermodynamics (ensembles, simple systems) 
• describe the main approaches in classical Molecular Modeling (Monte Carlo, Molecular Dynamics) in various ensembles
• discuss examples of computer programs in detail,
• evaluate the application of numerical simulations,
• list the possible start and boundary conditions for a numerical simulation.
  

The students are able to:

• set up computer programs for solving simple problems by Monte Carlo or molecular dynamics,
• solve problems by molecular modeling,
  
• set up a numerical grid,
• perform a simple numerical simulation with OpenFoam,
• evaluate the result of a numerical simulation.
  
  

The students are able to

• develop joint solutions in mixed teams and present them in front of the other students,
• to collaborate in a team and to reflect their own contribution toward it.

The students know the thermodynamic base principles for steam generators and their types. They are able to describe the basic principles of steam generators and sketch the combustion and fuel supply aspects of fossil-fuelled power plants. They can perform thermal design calculations and conceive the water-steam side, as well as they are able to define the constructive details of the steam generator. The students can describe and evaluate the operational behaviour of steam generators and explain these in the context of related disciplines.

The students will be able, using detailed knowledge on the calculation, design, and construction of steam generators, linked with a wide theoretical and methodical foundation, to understand the main design and construction aspects of steam generators. Through problem definition and formalisation, modelling of processes, and training in the solution methodology for partial problems a good overview of this key component of the power plant will be obtained.

Within the framework of the exercise the students obtain the ability to draw the balances, and design the steam generator and its components. For this purpose small but close to lifelike tasks are solved, to highlight aspects of the design of steam generators.

Especially during the exercises the focus is placed on communication with the tutor. This animates the students to reflect on their existing knowledge and ask specific questions to further improve their understanding. 

With completion of this module students can explain basics of risk management involving thematical adjacent contexts and can describe an optimal management of energy systems.

Furthermore, students can reproduce solid theoretical knowledge about the potentials and applications of new information technologies in logistics and explain technical aspects of the use, production and processing of hydrogen.

With completion of this module students are able to evaluate risks of energy systems with respect to energy economic conditions in an efficient way. This includes that the students can assess the risks in operational planning of power plants from a technical, economic and ecological perspective.

In this context, students can evaluate the potentials of logistics and information technology in particular on energy issues.

In addition, students are able to describe the energy transfer medium hydrogen according to its applications, the given security and its existing service capacities and limits as well as to evaluate these aspects from a technical, environmental and economic perspective.

Students are able to discuss issues in the thematic fields in the renewable energy sector addressed within the module.

Students can apply the learned knowledge of storage systems for excessive energy to explain for various energy systems different approaches to ensure a secure energy supply. In particular, they can plan and calculate domestic, commercial and industrial heating equipment using energy storage systems in an energy-efficient way and can assess them in relation to complex power systems. In this context, students can assess the potential and limits of geothermal power plants and explain their operating mode.

Furthermore, the students are able to explain the procedures and strategies for marketing of energy and apply it in the context of other modules on renewable energy projects. In this context they can unassistedly carry out analysis and evaluations of energie markets and energy trades. 

Students are able to discuss issues in the thematic fields in the renewable energy sector addressed within the module.